* Note that we report one-tailed p values throughout, except as discussed in table notes
*Model 1, Table 8, use file "Table 8 Wood Grose News Coverage 2010 & 2014 Data.dta"

nbreg storycount14 cfscandal otherscandal_notcf cook circulation14 qcandidate14 spend14 percentwhite14 mediainc14 percentcollege14 yeardummy uncontested14 if openseat14==0, robust cluster(ccesdistrict14) level(90)

*Model 2, Table 8, use file "Table 8 Wood Grose News Coverage 2010 & 2014 Data.dta"
nbreg traits14 cfscandal otherscandal_notcf cook circulation14 qcandidate14 spend14 percentwhite14 mediainc14 percentcollege14 yeardummy uncontested14 if openseat14==0, robust cluster(ccesdistrict14) level(90)

*Model 3, Table 8, use data "Lexis Local News Advance 2018 Data House media CF scandal.dta"
nbreg totalhitsinstate cfscandal otherscandal_notcf cookrating18 circulation14 qchal spenddandrtotal percentwhite14 mediainc14 percentcollege14 uncontested if openseat==0, robust level(90)

*Model 4, Table 8, use data "Lexis Local News Advance 2018 Data House media CF scandal.dta"
nbreg neg_persbus_state cfscandal otherscandal_notcf cookrating18 circulation14 qchal spenddandrtotal percentwhite14 mediainc14 percentcollege14 uncontested if openseat==0, robust level(90)

*Codebook describing each of the above variables

*For Models 1 and 2:
*Unless otherwise stated, the variables are all measures taken from:
*Hayes, Danny & Jennifer Lawless. 2018. "The Decline of Local News and Its Effects." Journal of Politics 80:332-36.
*For variables below that say "(from Hayes and Lawless 2018)," please see their article and supplemental data.
*storycount14 = Total # of stories, from Hayes & Lawless (2018)
*cfscandal = 1 if the legislator faced a campaign finance scandal and 0 if not. Source: govtrack.us
*otherscandal_notcf = 1 if legislator faced non-campaign finance scandal and 0 if not. Source: govtrack.us
*cook = Cook race rating (from Hayes and Lawless JOP 2018)
*circulation14 = Circulation (from Hayes and Lawless JOP 2018)
*qcandidate14 = Quality candidate (from Hayes and Lawless JOP 2018)
*spend14 = Campaign spending (from Hayes and Lawless JOP 2018)
*percentwhite14 = % white in district (from Hayes and Lawless JOP 2018)
*mediainc14 = median income in district (from Hayes and Lawless JOP 2018)
*percentcollege14 = % college in district (from Hayes and Lawless JOP 2018)
*yeardummy = 1 if year 2014 and 0 if 2010 (from Hayes and Lawless JOP 2018)
*uncontested14 = uncontested district (from Hayes and Lawless JOP 2018)
*openseat14 = open House seat(from Hayes and Lawless JOP 2018) 
*ccesdistrict14 = congressional district

*For Models 3 and 4: 
*totalhitsinstate = the total count of local news stories for each House incumbent that mentions scandals or violations
*neg_persbus_state = the total number of negative news stories mentioning the incumbent and scandals or violations
*cfscandal = 1 if the legislator faced a campaign finance scandal and 0 if not. Source: govtrack.us
*otherscandal_notcf = 1 if legislator faced non-campaign finance scandal and 0 if not. Source: govtrack.us
*For all these variables below, the measure is that used in Hayes and Lawless (2018).
*cookrating18 
*circulation14 
*qchal 
*spenddandrtotal 
*percentwhite14 
*mediainc14 
*percentcollege14 
*uncontested 
*openseat

